ライフサイエンスコーパス: automated detection

1 ally disposable and can carry out in situ or automated detection.

2 The automated detection agreed with expert interpretation of

3 sis or a back-propagation neural network, an automated detection algorithm can be developed with data

4 d we compared their performance with that of automated detection algorithms in data from middle- to o

5 al techniques are presently available for an automated detection and analysis of subsystem variants.

6 tate of molecular processes within the cell, automated detection and analysis of these processes, and

7 uorescent plate/slide imaging technology and automated detection and analysis software, we were able

8 able to semiautomated random acquisition and automated detection and quantification.

9 Real-time automated detection and quantitation of the bacteria are

10 igorous but flexible definition and accurate automated detection and tracking of geometric features o

11 dy illustrates how UAS, thermal imagery, and automated detection can be combined to efficiently colle

12 We demonstrate fully automated detection for both experimental and simulated

13 We found atrial fibrillation using automated detection (>/= 90 s in 30 min) and classed as

14 ase MR images can be obtained routinely with automated detection of a contrast material bolus.

15 illaric circuit (CC) optimized for rapid and automated detection of bacteria in urine.

16 The application of WDM for rapid, automated detection of bacterial DNA from whole blood ma

17 Annotation System (BBAS), which enables the automated detection of bees' behaviours in small observa

18 ate image information is a powerful tool for automated detection of cell morphology changes.

19 Automated detection of colonic polyps, especially clinic

20 This paper presents a method for automated detection of complex (non-self-avoiding) postu

21 hanced MR angiographic studies obtained with automated detection of contrast agent arrival.

22 Automated detection of decreases in SDAAM was 70% sensit

23 nces with nanometer-scale resolution and the automated detection of defects and edges, as well as det

24 ply deep learning to create an algorithm for automated detection of diabetic retinopathy and diabetic

25 g computerized image analysis algorithms for automated detection of disease extent from digital patho

26 Here we describe software tools for the automated detection of DNA restriction fragments resolve

27 a obtained at rest was used as a control for automated detection of dobutamine-induced wall motion ab

28 relation between counts by visual raters and automated detection of ePVSs in the same section (r = 0.

29 rsus that of a retina specialist (RS) in the automated detection of fluid on optical coherence tomogr

30 a control dataset, we developed a method for automated detection of key nodes in the motor network, i

31 sequences and respiratory motion, fast near-automated detection of myocardial segments and accurate

32 orithm analytic platform (ventMAP) to enable automated detection of off-target ventilation (OTV) deli

33 In fully automated detection of PCa patients, deep learning had a

34 r analysis of retinal photographs for DR and automated detection of RDR can be implemented safely int

35 ibility of the current method for completely automated detection of reactive metabolites via computer

36 In protocol 3, we tested the feasibility of automated detection of regional WM abnormalities in 11 p

37 emiautomated, dynamic measurement of LVV and automated detection of regional WM abnormalities.

38 in virions and their fixed 1:1 ratio enabled automated detection of single-particle fusion in both fi

39 The automated detection of social avoidance allows a marked

40 Automated detection of stress-induced wall motion abnorm

41 neration was shown to very effective for the automated detection of structurally related nonribosomal

42 Here, we report SynEM, a method for automated detection of synapses from conventionally en-b

43 findings have important implications for the automated detection of the disc margin and estimates of

44 al rating scores were highly correlated with automated detection of total burden volume (r = 0.58, P

45 he highly sensitive electrochemiluminescence automated detection system.

46 both visual counting under a microscope and automated detection using a chip-based flow cytometer.

47 Age-related macular degeneration automated detection was applied to a 2-class classificat

48 ent of this assay reflects the potential for automated detection with rapid and reliable assaying and